Persistent Job Queue

A single-node, disk-backed background job queue for Node.js.

The system uses an in-memory FIFO queue for fast job scheduling, a pool of async workers to execute jobs concurrently, and a file-based append-only log for persistence and crash recovery.

Jobs store only intent (type + payload), while executable logic is provided at runtime via a handler registry. On startup, the queue replays persisted job state, resets in-flight jobs, and resumes processing without data loss.

This is infrastructure level code designed to be:

• Simple: No external dependencies (Redis, RabbitMQ, etc).
• Resilient: Jobs survive process crashes and restarts.
• Concurrent: Multiple workers run in parallel (async).

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Features

• 🚀 In-Memory Speed: Jobs are queued and retrieved from memory for high performance.
• 💾 Disk Persistence: All job states are written to a local append-only log to survive crashes.
• 🔄 Crash Recovery: Automatically replays logs on startup, resetting interrupted jobs to pending.
• 🛠 Worker Pool: Configurable concurrency (e.g., run 3 or 50 jobs in parallel).
• 🧩 Decoupled Logic: Jobs are just data (type + payload); handlers are code.

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Installation

npm install persistent-job-queue

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Quick Start (Simple Example)

For simple scripts or small apps, you can define everything in one file.

import { createJobQueue } from "persistent-job-queue";

// 1. Define your handlers (the actual logic)
const handlers = {
  send_email: async (payload) => {
    console.log(`Sending email to ${payload.to}...`);
    await new Promise(r => setTimeout(r, 1000)); // Simulate async work
  },
  resize_image: async (payload) => {
    console.log(`Resizing ${payload.file} to ${payload.width}px...`);
  }
};

// 2. Create the queue instance
const queue = createJobQueue(
  {
    storagePath: ".queue/jobs.log", // Where to store persistence file
    workerCount: 3,                 // How many jobs to run concurrently
  },
  handlers
);

// 3. Add jobs
queue.add({
  id: "job-1", 
  type: "send_email",
  payload: { to: "user@example.com" },
  attempts: 0,
  maxAttempts: 3
});

queue.add({
  id: "job-2",
  type: "resize_image",
  payload: { file: "avatar.png", width: 300 },
  attempts: 0,
  maxAttempts: 3
});

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Production Structure (Best Practices)

For larger applications, you should separate your concerns. A recommended structure ensures your code remains clean and maintainable.

Recommended Folder Layout

src/
├── jobs/                 # Individual job logic files
│   ├── sendEmail.ts
│   ├── generateReport.ts
│   └── heavyTask.ts
│
├── jobHandlers.ts        # The Registry (maps types to functions)
├── queue.ts              # The Queue Instance (Singleton)
└── server.ts             # App entry point

1. Define Jobs (src/jobs/*.ts)

Create isolated functions for each task. This keeps your business logic testable and clean.

src/jobs/sendEmail.ts

export async function sendEmail(payload: { to: string }) {
  console.log("Sending email to", payload.to);
  // Real email logic here...
}

src/jobs/heavyTask.ts

export async function heavyTask(payload: { duration: number }) {
  console.log("Starting heavy task...");
  await new Promise(r => setTimeout(r, payload.duration));
}

2. Create the Registry (src/jobHandlers.ts)

This file maps the "string intent" (Job Type) to the "executable function" (Handler). It acts as the contract for your system.

import { sendEmail } from "./jobs/sendEmail";
import { heavyTask } from "./jobs/heavyTask";

export const jobHandlers = {
  send_email: sendEmail,
  heavy_task: heavyTask,
};

3. Initialize the Queue (src/queue.ts)

Initialize the queue once and export the instance.

import { createJobQueue } from "persistent-job-queue";
import { jobHandlers } from "./jobHandlers";

export const queue = createJobQueue(
  {
    storagePath: ".queue/jobs.log", // Ensure this path is gitignored
    workerCount: 5,                 // Adjust based on your server capacity
  },
  jobHandlers
);

4. Use it in your App (src/server.ts)

Now you can import the queue anywhere in your application (API routes, other services, etc) to offload work.

import { queue } from "./queue";

// Verify queue is running...

// Inside an API route or controller:
queue.add({
  id: "unique-job-id-123",
  type: "heavy_task",
  payload: { duration: 5000 },
  attempts: 0,
  maxAttempts: 3,
});

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How It Works

1. Enqueue: When you call queue.add(), the job is added to an in-memory queue (for speed) and appended to the disk log (for safety).
2. Processing: A pool of workers (size defined by workerCount) constantly pulls jobs from the in-memory queue.
3. Execution: The worker looks up the handler for the job's type in the registry you provided.
4. Completion/Failure: The job status is updated to completed or failed and persisted to disk.
5. Crash Recovery: If the app restarts, the library reads the disk log, finds any jobs that were running or pending, and re-queues them ensures nothing is lost.

Why use this over Cron/DB?

Feature	Cron + DB Polling	Persistent Job Queue
Trigger	Time-based (Every X mins)	Event-based (Immediate)
Latency	High (Wait for next poll)	Low (Instant execution)
DB Load	Heavy (Constant polling queries)	Zero (Disk-append mostly)
Crash Safety	Manual handling required	Built-in (Automatic Recovery)
Concurrency	Hard to manage	Built-in Worker Pool

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License

MIT